BUS5PB – Principles of Business Analytics
Referencing Styles :
Harvard | Pages :
second assignment aims to enhance your understanding of the roles and
importance of business analytics and its implementations in industry.
You are also required to work on descriptive analytics in practice. The
assignment comprises three tasks. First task is to develop an extensive
review report of landscape of business analytics in industry. Second
task is to develop a taxonomy for descriptive analytics techniques,
describing for each technique its purpose, functionality, assumptions,
method of validation and sample use case. The third task is to develop a
linear regression model to predict the propensity score of a specified
Compile a review report that:
• describes the purpose, importance and role of business analytics in creating strategic value and competitive advantage.
• defines analytics ecosystem
(descriptive, predictive, prescriptive and exploratory analytics) and
illustrates how they are adopted by various industries in their key
business functions ranging from strategy, marketing and sales,
operations (production), customer services etc.
• illustrates how the data mining
process can be implemented and in particular, challenges in implementing
data mining and business analytics in agile business environments.
• describe challenges of
achieving/cultivating analytic leadership and culture in practice. Hint:
please review all presentation slides and select the relevant knowledge
points. You may also need to perform research on literatures and
industrial cases to explain and support your points. Use academic,
industrial and technical references and real case examples to support
your views on each of the above. The report is required to be written in
a professional format conforming to report guidelines noted below.
Task 02 aims to assess your knowledge on
descriptive analytics covered/mentioned in class. You are required to
develop a taxonomy for descriptive analytics techniques, describing for
each technique its purpose, functionality, assumptions, method of
validation and sample use case. The sample use case must be from a
business analytics scenario. An example is shown below.
Task 03 aims to assess your practical
knowledge and skills to develop a linear regression model using R and
Excel. You are required to develop a linear regression model using the
data provided. The data was captured to evaluate the propensity scores
of employees from a company to contract a flu during a winter season.
The data (‘Immunity.csv’) consists of three columns:
• Propensity: is the probability of an employee to catch the flu;
• Non_healthy_food: the average monthly expenses employees spent on none healthy food in dollar value during past 2 months;
• Percent_inoffice: the percentage of time an employee works in the office during past 2 months.
Task 03 requirements:
• Explore and plot the correlation among variables (in Excel or R);
• Develop a linear regression model using R and Excel (you are required to submit both the excel file and the R scripts)
• Illustrate and explain the key
parameters of the model including coefficient of determination (R2),
Residues, fitted linear equation, etc.
• Identify the significant independent variable;
• Predict the propensity scores of three
new employees contracting the flu from the following test data (using
95% confidence interval):
Non_healthy_food = 290, Percent_inoffice = 6
Non_healthy_food = 500, Percent_inoffice = 64
Non_healthy_food = 300, Percent_inoffice = 80
Hint: in the last item of Task 03, propensity score prediction, Excel only has function for point prediction
of mean but R can be used for both point
and interval prediction. To avoid complex calculations in Excel, please
use R for prediction with confidence intervals.
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